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engine-fault-xgboost
ShanRaja/engine-fault-xgboost
engine-fault-xgboost is a machine learning model from ShanRaja. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model predicts engine condition (Active / Faulty) using sensor data.
Access
Public
Updated Jan 16, 2026
Repo size
121 KB
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.joblib121 KB · 94%
At a glance
- Access
- Public
- Created
- Jan 14, 2026
- Updated
- Jan 16, 2026
- SHA
- 6e0db9d4
Model card
From the Hugging Face model README
Engine Fault Detection Model
Model Overview
This model predicts engine condition (Active / Faulty) using sensor data.
Algorithm
XGBoost Classifier
Training Details
- Objective: Maximize Recall
- Class Imbalance: Handled via class weights / scale_pos_weight
- Outlier Treatment: Coolant temperature capped using training data thresholds
Dataset
- Source: ShanRaja/Data
- Splits: Train / Validation / Test
Metrics (Validation)
- Recall: 1.0
- Precision: 0.63
- F1-score: 0.77
Intended Use
Early fault detection to minimize false negatives.
- Created
- Jan 14, 2026
- Updated
- Jan 16, 2026